Australian dispute exposes big tech's power and fears
Bibliographic record
Abstract
Significance Facebook on February 17 cut off its users from Australian news, in protest against the proposed legislation. News publishers want ‘big tech’ that earn advertising revenue from their news to pay for the journalism. They currently earn digital revenue from the click-through traffic that Facebook and Google dribble in their direction. Impacts Facebook's ability to block news in Australia entirely and at scale contrasts sharply with its other dilatory content moderation efforts. Without a settlement, Facebook risks Australia widening the draft law to include other Facebook ad-supported properties, eg, Instagram. Publishers are unlikely to devote all the money received from big tech to expanding newsroom budgets. Canada intends to adopt Australia's approach as it drafts its own legislation in coming months.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".